KIRC Analysis in Metastatic Patients

In [1]:
cd ../src

/cellar/users/agross/TCGA_Code/TCGA/Pathway_Merge/src
In [26]:
import os as os
import pickle as pickle
import subprocess
import pandas as pd

from Reports.Figures import *
from Processing.Tests import *
from Reports.NotebookTools import *

pd.set_option('precision',3)

Manually definded drug categories

In [4]:
drugs = {'TKI': ['perifosine', 'sunitinib', 'sorafenib', 'pazopanib', 'sutent', 'tarceva','nexavaar',
         'sutent (sunitinib)', 'gefitinib', 'nexavar', 'bay-439006', 
         'azd', 'iressa', 'sorafenib - nexavar', 'axitinib', 'sunitinib (sutent)', 'tipifarnib',
         'tyrosine kinase inhibitor', 'votrient', 'zd6474'],
         'VEGF Ab': ['bevacizumab', 'avastin'],
         'mTORi': ['temsirolimus','everolimus','rad001','torisel','afinitor'],
         'IL2/IF': ['interferon', 'il-2','il-2 (high dose)','proleukin (il-2)',
          'interleukin-2','interferon-alpha','interferon alpha',
          'intron a', 'alpha interferon', 'proleukin'
          'roferon-a','il-2 thearpy (interleukin)','high dose interleukin-2',
          'ifn-alpha (intron)', 'interleukin 2-high dose', 'inf'],
          'Chemo': ['bortezomib', 'gemictiabine', '5-fluorouracil','capecitabine','gemzar','thalidomide','nab-rapamycin',
                    'capecitabin', 'gemcitabine','xeloda'],
          'Vaccine': ['oncophage', 'oncophage vaccine']}
drug_map = {drug:family for family, drugs in drugs.items() for drug in drugs}

Initialization

In [5]:
result_path = '/scratch/TCGA/Firehose__2012_01_16/ucsd_analyses'
run = sorted(os.listdir(result_path))[1]
run = pickle.load(open('/'.join([result_path, run, 'RunObject.p']), 'rb'))
In [8]:
cancer = run.load_cancer('KIRC')
clinical = cancer.load_clinical()
global_vars = cancer.load_global_vars()
In [9]:
mut = cancer.load_data('MAF')
mut.uncompress()
meth = cancer.load_data('Methylation')
cn = cancer.load_data('CN_broad')
cn.uncompress()
rna = cancer.load_data('mRNASeq')
rppa  = cancer.load_data('RPPA')

Section 1: Stratification Variables

Clinical Variables

Tumor Stage

In [10]:
stage = clinical.clinical.tumor_stage
stage = stage.map(lambda s: s.replace('stge', 'stage'))
stage.value_counts().sort_index().plot(kind='bar')
Out[10]:
<matplotlib.axes.AxesSubplot at 0x6020450>
In [32]:
stage = clinical.clinical.tumor_stage.map({'stge i': 'Stage I', 'stge ii': 'Stage II', 'stge iii': 'Stage III', 'stge iv': 'Stage IV'})
surv = clinical.survival.survival_5y

f = stage
t = get_surv_fit(surv, f)
f.name = 'Overall Survival'
f = draw_survival_curves(f, surv, colors=['green','blue','orange','red'], labels=list(f.unique()), show=True)
fig_tab(f, t)
Out[32]:
95% Confidence Int.
# Patients # Events Median Time Lower Upper
Stage I 241 29 NaN NaN NaN
Stage II 49 6 NaN NaN NaN
Stage III 121 45 4.48 3.21 NaN
Stage IV 76 54 1.78 1.26 3.28
In [11]:
fig, axs = subplots(1,2, figsize=(12,4))
v = clinical.clinical.tumor_grade
v.value_counts().sort_index().plot(kind='bar', title=v.name, ax=axs[0]);
v = clinical.clinical.tumor_grade.dropna().map(lambda s: s[:2])
v.value_counts().sort_index().plot(kind='bar', title=v.name, ax=axs[1]);
axs[0].set_ylabel('# of Patients')
Out[11]:
<matplotlib.text.Text at 0x5fcaed0>
In [17]:
age = clinical.clinical.age
by_stage = pd.DataFrame({s: age[stage[stage==s].index].describe() for s in stage.unique()})
all_stage = pd.Series(clinical.clinical.age.describe(), name='All')
by_stage.join(all_stage).astype(object)
Out[17]:
stage i stage ii stage iii stage iv All
count 241 49 120 76 486
mean 60.3 60.7 63.3 61.2 61.2
std 12.8 12.5 12.1 9.9 12.2
min 26.6 39.3 32.1 33.5 26.6
25% 51.4 49.5 56 55.7 52.2
50% 59.8 59.7 63.9 61.3 61.2
75% 70.2 69.8 72.9 66.2 70.4
max 90.1 86.5 88.7 84.2 90.1
In [18]:
age.hist()
ylabel('# of Patients')
xlabel('Age')
Out[18]:
<matplotlib.text.Text at 0x610bf10>
In [34]:
draw_survival_curves(age, surv, show=True)
Out[34]:

Effect of Age Broken Down by Stage

In [35]:
by_stage = pd.DataFrame({s: age[stage[stage==s].index].describe() for s in stage.unique()})
all_stage = pd.Series(clinical.clinical.age.describe(), name='All')
tab = by_stage.join(all_stage).astype(object)
fig = draw_survival_curves(age, surv, stage, show=True)
fig_tab(fig, tab)
Out[35]:
Stage I Stage II Stage III Stage IV All
count 241 49 120 76 486
mean 60.3 60.7 63.3 61.2 61.2
std 12.8 12.5 12.1 9.9 12.2
min 26.6 39.3 32.1 33.5 26.6
25% 51.4 49.5 56 55.7 52.2
50% 59.8 59.7 63.9 61.3 61.2
75% 70.2 69.8 72.9 66.2 70.4
max 90.1 86.5 88.7 84.2 90.1
In [20]:
clinical.clinical.gender.value_counts().plot(kind='bar')
ylabel('# of Patients')
xlabel('Gender')
Out[20]:
<matplotlib.text.Text at 0x61ef190>
In [21]:
pd.crosstab(stage, clinical.clinical.calcium_level)
Out[21]:
calcium_level elevated low normal
tumor_stage
stage i 1 109 50
stage ii 0 16 15
stage iii 3 46 35
stage iv 6 22 30
In [22]:
s = pd.crosstab(stage, clinical.clinical.calcium_level).ix['stage iv']
s[['low','normal','elevated']].plot(kind='bar')
ylabel('# of Patients')
xlabel('Status')
Out[22]:
<matplotlib.text.Text at 0x86f4d90>
In [23]:
pd.crosstab(stage, clinical.clinical.eastern_cancer_oncology_group.dropna())
Out[23]:
eastern_cancer_oncology_group 0 1 2
tumor_stage
stage i 18 2 2
stage ii 3 2 0
stage iii 13 3 0
stage iv 15 9 3
In [24]:
clinical.clinical.hemoglobin.value_counts().plot(kind='bar')
ylabel('# of Patients')
xlabel('Hemoglobin Level')
Out[24]:
<matplotlib.text.Text at 0x8700550>

Drugs administered

In [36]:
drugs_types = drugs.keys()
drug_categories = clinical.drugs.drugname.map(drug_map)
drug_given = pd.DataFrame({d: ((drug_categories == d).groupby(level=0).sum() > 0) for d in drugs_types})
In [37]:
fig, axs = subplots(1,2, figsize=(12,4))
crosstab(stage, drug_given.sum(1) > 0)[True].plot(kind='bar', ax=axs[0])
axs[0].set_ylabel('# of Patients')
axs[0].set_title('Patients Receiving Medication By Stage')

drug_given.sum().plot(kind='bar', ax=axs[1]);
axs[1].set_title('Drug Categories');

In Stage IV

In [43]:
fig, axs = subplots(1,2, figsize=(12,4))
s = drug_given.ix[stage.index[stage == 'Stage IV']].sum()
s.plot(kind='bar', ax=axs[0])
axs[0].set_ylabel('# of Patients')
axs[0].set_title('Drugs Given in Stage IV')

n = drug_given.ix[stage.index[stage == 'Stage IV']].dropna().sum(1).value_counts()[:5]
n.plot(kind='bar')
axs[1].set_title('Drugs Given per Patient')
Out[43]:
<matplotlib.text.Text at 0xa1ec810>

Single Drug Patients

In [45]:
gc = drug_given.astype(int).astype(str).apply(lambda s: ''.join(s), axis=1)
gc.name = 'drugs'
one_drug = drug_given.ix[stage.index[stage == 'Stage IV']].dropna().sum(1) == 1

vc = drug_given.ix[one_drug[one_drug].index].sum().order()
vc.plot(kind='bar')
ylabel('# of Patients');
In [51]:
split_cols = lambda s: ','.join([d for i,d in enumerate(drug_given.columns) if s[i] == '1'])
t = get_surv_fit(surv, gc[one_drug[one_drug].index])
t.index = map(split_cols, t.index)
f = draw_survival_curves(gc[one_drug[one_drug].index], surv, colors=['red','orange','green','purple','blue','yellow'], 
                     labels=[c for c in drug_given.columns if vc[c] > 0][::-1], show=True, show_legend='out')
fig_tab(f,t)
Out[51]:
95% Confidence Int.
# Patients # Events Median Time Lower Upper
mTORi 1 1 0.91 NaN NaN
Vaccine 3 3 1.78 1.57 NaN
TKI 15 9 2.95 0.94 NaN
IL2/IF 12 6 3.92 1.94 NaN
Chemo 1 1 0.25 NaN NaN

Molecular Characterization

VHL

In [60]:
vhl_mut = mut.df.ix['VHL'].map({0:'WT',1:'Mutated'})
vhl_mut.name = 'VHL_mut'
vhl_meth = meth.df.ix['VHL']
vhl_meth.name = 'VHL_meth'
vhl_rna = rna.df.ix['VHL']
vhl_rna.name = 'VHL_rna'
In [64]:
a = draw_survival_curves(vhl_mut, surv, ann='p', show=True)
b = draw_survival_curves(vhl_mut, surv, stage, ann='p', show=True)
stack([a,b])
Out[64]:

CDK Deletion

In [68]:
labels = Series({-2: 'Homozygous Deletion', -1: 'Deletion', 0: 'Normal', 1: 'Amp', 2: 'High Amp'})
colors = Series({-2: 'black', -1: 'purple', 0: 'blue', 1: 'orange', 2: 'red'})
In [67]:
cdk_del = cn.df.ix['Deletion'].ix['9p21.3'].ix[0]
cdk_del.name = 'del_band'
In [70]:
f = cdk_del
a = draw_survival_curves(f, surv, ann='p', show=True, colors=colors[sorted(f.unique())].tolist(), 
                         labels=labels[sorted(f.unique())].tolist())
b = draw_survival_curves(f, surv, stage, ann='p', show=True, colors=colors[sorted(f.unique())].tolist(), 
                         labels=labels[sorted(f.unique())].tolist())
stack([a,b])
Out[70]:

Mutations in Stage 4

In [74]:
metastatic = stage[stage == 'Stage IV'].index
met = (mut.df.ix[:,metastatic] > 0).sum(1).order()
met = met[met>2]
g = (mut.df > 0).sum(1).order()
g = g.ix[met.index]
In [75]:
m = pd.concat([met, g-met, g], keys=['Metastatic','Non-Metastatic', 'All'], axis=1)
In [77]:
figsize(15,4)
g = (mut.df.ix[:,metastatic] > 0).sum(1).order()
g = g[g>2]
g.plot(kind='bar')
ylabel('# of Patients')
Out[77]:
<matplotlib.text.Text at 0xa9ac190>
In [79]:
pathway_plot(mut.df.ix[g[g>3].index,metastatic], False)
In [82]:
metastatic = stage[stage == 'Stage IV'].index
In [83]:
survival_test = 'survival_5y'
covariates = ['age', ('mutation', 'rate_non')]
cov_df = global_vars.join(clinical.clinical, how='outer').join(cdk_del)
cov_df = cov_df[covariates]
remerge = lambda s: '__'.join(s) if type(s) != str else s
cov_df = cov_df.rename(columns=remerge)
surv = clinical.survival[survival_test]
test = SurvivalTest(surv, cov_df)
test.name = survival_test
test.check_feature = lambda s: True
In [84]:
df = mut.features.ix[:,stage[stage.isin(['Stage IV'])].index]
df = df.dropna(axis=1)
counts = Series(df.sum(1), name='counts')
df  = df[counts > 6]
In [85]:
mut_met = run_feature_matrix(df, test)
del mut_met[('Full','fmla')]
mut_met = mut_met.join(counts).sort(columns=[('Full','LR')])
In [89]:
mut_met.head(10).astype(object)
Out[89]:
(Full, LR) (Full, LR_q) (Univariate, hazzard) (Univariate, p) (Univariate, q) counts
WNT_SIGNALING 0.000552 0.049 6.17 0.000285 0.0504 8
REACTOME_HOST_INTERACTIONS_OF_HIV_FACTORS 0.000554 0.049 1.39 0.452 0.972 12
SETD2 0.00104 0.0614 0.359 0.101 0.94 9
REACTOME_COSTIMULATION_BY_THE_CD28_FAMILY 0.00796 0.264 2.76 0.0387 0.893 7
VHL 0.00953 0.264 0.644 0.312 0.972 18
REACTOME_CLASS_A1_RHODOPSIN_LIKE_RECEPTORS 0.0106 0.264 1.93 0.127 0.972 17
REACTOME_APOPTOSIS 0.0124 0.264 2.71 0.0207 0.893 12
KEGG_FOCAL_ADHESION 0.0134 0.264 1.77 0.263 0.972 26
REACTOME_METABOLISM_OF_AMINO_ACIDS 0.0136 0.264 2.05 0.0961 0.94 13
KEGG_GAP_JUNCTION 0.0149 0.264 1.56 0.336 0.972 10
In [95]:
def draw_me(f):
    split_by_stage = draw_survival_curves(mut.features.ix[f], surv, stage, ann='p', show=True)
    all_surv = draw_survival_curves(mut.features.ix[f], surv, ann='p', show=True)
    curves = draw_survival_curves(mut.features.ix[f, df.columns], surv, ann='p', filename='tmp.png', show=True)
    try:
        figsize=(6,4)
        pathway_plot(mut.df.ix[run.gene_sets[f], df.columns], plt.gca())
        plt.tight_layout()
        plt.savefig('tmp1.png', dpi=75, bbox_inches=0, pad_inches=0)
        plt.close('all')
        return stack([side_by_side(['tmp.png', 'tmp1.png']), split_by_stage, all_surv])
    except:
        return stack([curves, split_by_stage, all_surv])
    
s = stack([draw_me(f) for f in mut_met.index[:15]])
s
Out[95]:

In [96]:
survival_test = 'survival_5y'
covariates =  ['age']
cov_df = global_vars.join(clinical.clinical, how='outer').join(cdk_del)
cov_df = cov_df[covariates]
remerge = lambda s: '__'.join(s) if type(s) != str else s
cov_df = cov_df.rename(columns=remerge)
surv = clinical.survival[survival_test]
test = SurvivalTest(surv, cov_df)
test.name = survival_test
test.check_feature = lambda s: True
In [98]:
df = rppa.features.ix[:,stage[stage.isin(['Stage IV'])].index]
df = df.dropna(axis=1)

rppa_met = run_feature_matrix(df, test)
rppa_met = rppa_met.join(counts).sort(columns=[('Full','LR')])
In [100]:
rppa_met.head(10)
Out[100]:
(Full, LR) (Full, LR_q) (Full, fmla) (Univariate, hazzard) (Univariate, p) (Univariate, q) counts
phos_pc PRKAA1 2.54e-04 0.04 Surv(days, event) ~ age * feature\n 1.36e-04 8.98e-03 0.14 73
protiens (PRKAA1, AMPK_pT172-R-V) 3.10e-04 0.04 Surv(days, event) ~ age * feature\n 5.05e-01 8.39e-03 0.14 73
(AR, AR-R-V) 3.43e-04 0.04 Surv(days, event) ~ feature\n 3.57e-01 4.10e-04 0.05 73
(NFKB1, NF-kB-p65_pS536-R-C) 4.81e-04 0.04 Surv(days, event) ~ feature\n 5.08e-01 4.99e-04 0.05 73
(GATA3, GATA3-M-V) 1.32e-03 0.08 Surv(days, event) ~ feature\n 5.18e+00 1.52e-04 0.05 73
(RPS6, S6-R-NA) 2.35e-03 0.12 Surv(days, event) ~ feature\n 2.79e+00 1.44e-03 0.08 73
(PEA15, PEA-15-R-V) 2.59e-03 0.12 Surv(days, event) ~ feature\n 3.84e+00 1.69e-03 0.08 73
(STK11, LKB1-M-NA) 3.24e-03 0.12 Surv(days, event) ~ feature\n 2.84e+01 2.06e-03 0.08 73
phos_pc ERBB3 3.30e-03 0.12 Surv(days, event) ~ feature\n 1.77e-06 4.74e-03 0.14 73
pathways BIOCARTA_FAS_PATHWAY 4.05e-03 0.13 Surv(days, event) ~ feature\n 3.03e-04 2.43e-03 0.09 73
In [99]:
def draw_me(f):
    feature = rppa.features.ix[f, df.columns]
    feature.name = str(feature.name)
   
    curves = draw_survival_curves(feature, surv, show=True, show_legend=True, ann='p')
    feature = rppa.features.ix[f]
    feature.name = str(feature.name)
    split_by_stage = draw_survival_curves(feature, surv, stage, show=True, show_legend=True, ann='p')
    all_surv = draw_survival_curves(feature, surv, show=True, show_legend=True, ann='p')
    return stack([curves, split_by_stage, all_surv])

s = stack([draw_me(f) for f in rppa_met.index[:10]])
s
Out[99]:

Methylation

In [101]:
survival_test = 'survival_5y'
covariates =  ['age', ('methylation', 'pc1')]
cov_df = global_vars.join(clinical.clinical, how='outer').join(cdk_del)
cov_df = cov_df[covariates]
remerge = lambda s: '__'.join(s) if type(s) != str else s
cov_df = cov_df.rename(columns=remerge)
surv = clinical.survival[survival_test]
test = SurvivalTest(surv, cov_df)
test.name = survival_test
test.check_feature = lambda s: True
In [102]:
df = meth.features.ix[:,stage[stage.isin(['Stage IV'])].index]
df = df.dropna(axis=1)
meth_met = run_feature_matrix(df, test)
meth_met = meth_met.sort(columns=[('Full','LR')])
In [363]:
meth_met.head(10)
Out[363]:
Full Univariate
LR LR_q fmla hazzard p q
BIOCARTA_GLYCOLYSIS_PATHWAY 1.30e-04 0.03 Surv(days, event) ~ feature + methylation__pc1... 1.28e+05 2.15e-04 0.01
BIOCARTA_CDMAC_PATHWAY 2.59e-04 0.03 Surv(days, event) ~ feature\n 1.43e+05 3.53e-04 0.02
REACTOME_SHC_MEDIATED_SIGNALLING 6.21e-04 0.04 Surv(days, event) ~ feature + age + feature:age\n 1.16e+04 2.84e-05 0.01
REACTOME_GRB2_EVENTS_IN_EGFR_SIGNALING 1.12e-03 0.05 Surv(days, event) ~ feature + age + feature:age\n 6.65e+03 7.16e-05 0.01
REACTOME_AKT_PHOSPHORYLATES_TARGETS_IN_THE_CYTOSOL 1.20e-03 0.05 Surv(days, event) ~ feature\n 1.76e+04 1.29e-03 0.04
REACTOME_MTORC1_MEDIATED_SIGNALLING 1.56e-03 0.05 Surv(days, event) ~ feature\n 7.58e+03 1.16e-03 0.04
REACTOME_ACTIVATED_TAK1_MEDIATES_P38_MAPK_ACTIVATION 2.33e-03 0.07 Surv(days, event) ~ feature + methylation__pc1... 5.81e+04 9.17e-03 0.12
REACTOME_GAMMA_CARBOXYLATION_TRANSPORT_AND_AMINO_TERMINAL_CLEAVAGE_OF_PROTEINS 3.37e-03 0.08 Surv(days, event) ~ feature\n 7.43e-06 3.48e-03 0.09
BIOCARTA_BARR_MAPK_PATHWAY 5.88e-03 0.12 Surv(days, event) ~ feature\n 2.81e+03 5.75e-03 0.11
REACTOME_RNA_POLYMERASE_III_CHAIN_ELONGATION 6.31e-03 0.12 Surv(days, event) ~ feature\n 2.87e+03 7.93e-03 0.12
In [375]:
def draw_me(f):
    feature = meth.features.ix[f, df.columns]
    feature.name = str(feature.name)
   
    curves = draw_survival_curves(feature, surv, show=True, show_legend=True, ann='p')
    feature = meth.features.ix[f]
    feature.name = str(feature.name)
    split_by_stage = draw_survival_curves(feature, surv, stage, show=True, show_legend=True, ann='p')
    pathway = Image(filename='{}/Figures/PathwayPlots/{}.png'.format(meth.path, f))
    all_surv = draw_survival_curves(feature, surv, show=True, show_legend=True, ann='p')
    return stack([curves, split_by_stage, all_surv])

s = stack([draw_me(f) for f in meth_met.index[:10]])
s
Out[375]:

In [103]:
n = meth_met.index[0]
print n
Image(filename='{}/Figures/PathwayPlots/{}.png'.format(meth.path, n))

BIOCARTA_GLYCOLYSIS_PATHWAY
Out[103]:

Expression

In [105]:
survival_test = 'survival_5y'
covariates =  ['age']
cov_df = global_vars.join(clinical.clinical, how='outer').join(cdk_del)
cov_df = cov_df[covariates]
remerge = lambda s: '__'.join(s) if type(s) != str else s
cov_df = cov_df.rename(columns=remerge)
surv = clinical.survival[survival_test]
test = SurvivalTest(surv, cov_df)
test.name = survival_test
test.check_feature = lambda s: True
In [106]:
df = rna.features.ix[:,stage[stage.isin(['Stage IV'])].index]
df = df.dropna(axis=1)
rna_met = run_feature_matrix(df, test)
rna_met = rna_met.sort(columns=[('Full','LR')])
In [107]:
rna_met.head(10)
Out[107]:
Full Univariate
LR LR_q fmla hazzard p q
KEGG_GLYCINE_SERINE_AND_THREONINE_METABOLISM 2.81e-04 0.04 Surv(days, event) ~ feature\n 9.73e-06 7.55e-05 0.02
KEGG_PRIMARY_BILE_ACID_BIOSYNTHESIS 3.14e-04 0.04 Surv(days, event) ~ feature\n 2.69e-06 1.90e-04 0.02
REACTOME_HDL_MEDIATED_LIPID_TRANSPORT 3.84e-04 0.04 Surv(days, event) ~ feature\n 2.17e-06 1.20e-04 0.02
KEGG_HISTIDINE_METABOLISM 4.06e-04 0.04 Surv(days, event) ~ feature\n 1.35e-05 1.61e-04 0.02
KEGG_RENIN_ANGIOTENSIN_SYSTEM 6.71e-04 0.06 Surv(days, event) ~ feature\n 1.59e-05 2.66e-04 0.02
KEGG_PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS 9.09e-04 0.06 Surv(days, event) ~ feature\n 1.15e-05 3.45e-04 0.02
REACTOME_GLUTATHIONE_CONJUGATION 1.10e-03 0.06 Surv(days, event) ~ feature\n 4.83e-05 5.89e-04 0.03
KEGG_ASCORBATE_AND_ALDARATE_METABOLISM 1.45e-03 0.07 Surv(days, event) ~ age * feature\n 1.36e-05 2.15e-04 0.02
REACTOME_BOTULINUM_NEUROTOXICITY 2.20e-03 0.07 Surv(days, event) ~ feature\n 2.69e+04 2.31e-03 0.06
REACTOME_LOSS_OF_NLP_FROM_MITOTIC_CENTROSOMES 2.31e-03 0.07 Surv(days, event) ~ feature\n 3.36e+04 3.64e-03 0.07
In [108]:
def draw_me(f):
    feature = rna.features.ix[f, df.columns]
    feature.name = str(feature.name)
   
    curves = draw_survival_curves(feature, surv, show=True, show_legend=True, ann='p')
    feature = rna.features.ix[f]
    feature.name = str(feature.name)
    split_by_stage = draw_survival_curves(feature, surv, stage, show=True, show_legend=True, ann='p')
    #pathway = Image(filename='{}/Figures/PathwayPlots/{}.png'.format(meth.path, f))
    all_surv = draw_survival_curves(feature, surv, show=True, show_legend=True, ann='p')
    return stack([curves, split_by_stage, all_surv])

s = stack([draw_me(f) for f in rna_met.index[:10]])
s
Out[108]:

In [109]:
pathway = Image(filename='{}/Figures/PathwayPlots/{}.png'.format(rna.path, rna_met.index[1]))
pathway
Out[109]: